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  {
   "source": [
    "## Linear Regression for North American Pumpkins - Lesson 1"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Import needed libraries"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model, model_selection\n"
   ]
  },
  {
   "source": [
    "Load the diabetes dataset, divided into `X` data and `y` features"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(442, 10)\n[ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076\n -0.04340085 -0.00259226  0.01990842 -0.01764613]\n"
     ]
    }
   ],
   "source": [
    "X, y = datasets.load_diabetes(return_X_y=True)\n",
    "print(X.shape)\n",
    "print(X[0])"
   ]
  },
  {
   "source": [
    "Select just one feature to target for this exercise"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[:, np.newaxis, 2]\n"
   ]
  },
  {
   "source": [
    "Split the training and test data for both `X` and `y`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
   ]
  },
  {
   "source": [
    "Select the model and fit it with the training data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "source": [
    "Use test data to predict a line"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "source": [
    "Display the results in a plot"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}